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Found 36 papers, 16 papers with code
Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
To address these problems, we propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) which integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities.
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items.
Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding
Inspired by the activity-silent and persistent activity mechanisms in human visual perception biology, we design a Unified Static and Dynamic Network (UniSDNet), to learn the semantic association between the video and text/audio queries in a cross-modal environment for efficient video grounding.
Hyper-3DG: Text-to-3D Gaussian Generation via Hypergraph
Our framework is anchored by a well-established mainflow and an essential module, named ``Geometry and Texture Hypergraph Refiner (HGRefiner)''.
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
These hard samples are then employed to promote the quality of the ensemble model by adjusting the ensembling weights for each client model.
Finding and Editing Multi-Modal Neurons in Pre-Trained Transformer
Multi-modal large language models (LLM) have achieved powerful capabilities for visual semantic understanding in recent years.
Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
In this manner, our framework is able to learn the unified representations of uni-modal or multi-modal skeleton input, which is flexible to different kinds of modality input for robust action understanding in practical cases.
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA
The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers.
Towards Complex-query Referring Image Segmentation: A Novel Benchmark
Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms.
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
5) Considered both middle- and large- scale outdoor environments, i. e., the 6 trajectories range from 246m to 6. 95km.
Redundancy-aware Transformer for Video Question Answering
This paper identifies two kinds of redundancy in the current VideoQA paradigm.
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval
As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner.
Domain Generalized Stereo Matching via Hierarchical Visual Transformation
In this way, we can prevent the model from exploiting the artifacts of synthetic stereo images as shortcut features, thereby estimating the disparity maps more effectively based on the learned robust and shortcut-invariant representation.
Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames
In turn, the frame-level optimization is through gradient descent using the meta loss of video retrieval model computed on the whole video.
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts.
Partially Relevant Video Retrieval
To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR).
Ranked #1 on
Partially Relevant Video Retrieval
on TVR
Mitigating Hidden Confounding Effects for Causal Recommendation
Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.
Impression Allocation and Policy Search in Display Advertising
In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.
Modeling Field-level Factor Interactions for Fashion Recommendation
Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones.
Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication
This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked.
Deconfounded Video Moment Retrieval with Causal Intervention
To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020.
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items.
Computation Resource Allocation Solution in Recommender Systems
On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.
Progressive Localization Networks for Language-based Moment Localization
The language-based setting of this task allows for an open set of target activities, resulting in a large variation of the temporal lengths of video moments.
Dual Encoding for Video Retrieval by Text
In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own.
Ranked #3 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Visual Relation Grounding in Videos
In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV).
Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos.
Knowledge Enhanced Neural Fashion Trend Forecasting
Further-more, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge EnhancedRecurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time-series data.
Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media
We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata.
Deep Conversational Recommender in Travel
When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries.
Person Re-identification with Metric Learning using Privileged Information
We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space.
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising
With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.
Enhancing Person Re-identification in a Self-trained Subspace
To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched.
On the Pricing of Storable Commodities
The price of a commodity is the expectation under a suitable pricing measure of the totality of the discounted risk-adjusted future convenience dividend, conditional on the information provided by the market filtration.
